A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting

Multi-Energy Systems (MES) allow optimal interactions between different energy sources. Accurate load forecasting for such intricate systems would greatly enhance the performance and economic incentive to employ them. This article proposes a state-of-the-art deep learning based architecture to forecast multiple loads. The algorithm utilizes load correlations to select optimal input parameters. These optimal inputs are fed to D-TCNet (Deep – Temporal Convolution Network). This network uses multi-layer perceptrons (MLP) to encode the spatial relationship among exogenous variables which is fed to a Temporal Convolutional Network (TCN). The TCN resolves temporal information in the multi-load time series which is used for forecasting these loads for fixed output horizon. The proposed novel method is used on the energy consumption data for multi energy system of University of Austin Tempe Campus. The proposed method shows improved performance across all three energy types as well as all four seasons.